A. Field of Invention
This invention pertains to the art of methods and apparatuses of utilities, and even more particularly to the art of tracking power outages, and even more particularly to tracking historical power outages at the individual customer level.
B. Description of the Related Art
It is known in the art to have outage management systems, wherein customers would call in and report to a call center that their lights were out, or that there was a problem of some kind. These bits of information were stored in the outage management system where they are then worked as a project. The call data that comes in, with the earlier software, is based on, for instance AM/FM, or a GIS model. The software was designed to know where all of the customers are located and how the electricity is delivered to them. The call data is compared against that model and a prediction is made to determine that the outage encompasses a set number of customers. The data is then given to a trouble man, or somebody in the field, to check out, analyze, fix, and restore. The document that earlier software historically creates is essentially one record that says a certain device was out, it went out at this time, it was restored at this time, doing these steps, and it affected this number of customers. All of this information is stored on one record. Each customer's call was one record. Each call then went into the system and was recorded either to an interactive voice response system or live customer service rep contact. If only one call was made, it showed up in the system as a single call. The system would compare numerous calls to see if there was anything in common with them, such as each call having a common up-line fuse for instance, and since the received calls have a common up-line fuse and that common up-line fuse serves 20 people, just because the other 15 did not call in does not mean that their lights are not out.
It is known to use a CRI (Circuit Reliability Index). The single records of outages were associated with a circuit, not the individual customer. The circuits would be grouped together and run through a formula that would then compare it against an average customer's expectations and determine if the entire circuit was performing above, below, or at the customer's expectations. Previously, the utilities would know that an entire circuit had failed the customer's expectations. The circuit performance was bad and the utility would have to go out and patrol the whole circuit or do a deep dive into the data that made up that circuit calculation to see if there was a specific problem that could be isolated.
In a typical electrical distribution system, electrical energy is generated by an electrical supplier or utility company and distributed to consumers via a power distribution network. The power distribution network is the network of electrical distribution wires which link the electrical supplier to its consumers. Typically, electricity from a utility is fed from a primary substation over a distribution cable to several local substations. At the substations, the supply is transformed by distribution transformers from a relatively high voltage on the distributor cable to a lower voltage at which it is supplied to the end consumer. From the substations, the power is provided to industrial users over a distributed power network that supplies power to various loads. Such loads may include, for example, various power machines or computer/electronic equipment.
At the consumer's facility, there will typically be an intelligent electronic device (“IED”), such as an electrical energy/watt-hour meter, connected between the consumer and the power distribution network so as to measure quantities such as the consumer's electrical consumption or electrical demand. Such a meter may be owned by the consumer and used to monitor and control consumption and report costs or may be owned by the utility and used to monitor consumption and report revenue.
IED's include devices such as Programmable Logic Controllers (“PLC's”), Remote Terminal Unit (“RTU”), meters, protective relays, and fault recorders. Such devices are widely available that make use of memory and microprocessors and have limited remote reporting capabilities. A PLC is a solid-state control system that has a user-programmable memory for storage of instructions to implement specific functions such as Input/output (I/O) control, logic, timing, counting, report generation, communication, arithmetic, and data file manipulation. A PLC consists of a central processor, input\output interface, and memory. A PLC is typically designed as an industrial control system. An exemplary PLC is the SLC 500 Series, manufactured by Allen-Bradley in Milwaukee, Wis.
A meter is a device that records and measures electrical power consumption. Energy meters include, but are not limited to, electric watt-hour meters. In addition, meters are also capable of measuring and recording power events, power quality, current, voltage waveforms, harmonics, transients, or other power disturbances. Revenue accurate meters (“revenue meter”) are revenue accuracy electrical power metering devices which may include the ability to detect, monitor, or report, quantify, and communicate power quality information about the power which they are metering. An exemplary revenue meter is model 8500, manufactured by Power Measurement Ltd, in Saanichton, B.C. Canada.
A protective relay is an electrical device that is designed to interpret input conditions in a prescribed manner, and after specified conditions are met, to cause contact operation or similar abrupt change in associated electric circuits. A relay may consist of several relay units, each responsive to a specified input, with the combination of units providing the desired overall performance characteristics of the relay. Inputs are usually electric but may be mechanical, thermal or other quantity, or a combination thereof. An exemplary relay is type N and KC, manufactured by ABB in Raleigh, N.C.
A fault recorder is a device that records the waveform resulting from a fault in a line, such as a fault caused by a break in the line. An exemplary fault recorder is IDM, manufactured by Hathaway Corp in Littleton, Colo.
IED's can also be created from existing electromechanical meters or solid-state devices by the addition of a monitoring and control device which converts the mechanical rotation of the rotary counter into electrical pulses. An exemplary electromechanical meter is the AB1 Meter manufactured by ABB in Raleigh, N.C. Such conversion devices are known in the art.
In the early 1980's, the Computer Business Manufacturers Association (CBEMA), which is now the Information Technology Industry Council (ITIC), established a susceptibility profile curve to aid manufacturers in the design of power supply protection circuits. This power quality curve has since become a standard reference within the industry, measuring all types of equipment and power systems and defines allowable disturbances that can exist on the power lines. Additionally, the semiconductor industry has established its own standard SEMI F47 curve for power quality, which is similar to the CBEMA curve but instead is focused on semiconductor power quality and associated supporting equipment.
In more recent years the electric utility marketplace has moved towards deregulation, where utility consumers will be able to choose electrical service providers. Until now, substantially all end users purchased electric power they needed from the local utility serving their geographic area. Further, there was no way for utilities to guarantee the same reliability to all consumers from the utility because of different connection points to the transmission and distribution lines. With deregulation it is essential for consumers to be able to measure and quantify power reliability from their suppliers in order to ensure they are receiving the service they have opted for. Such service may involve various pricing plans, for example on volume, term commitments, peak and off-peak usage or reliability.
Power reliability is typically measured by several various indices. These indices include System Average Interruption Frequency Index (“SAIFI”), Customer Average Interruption Duration Index (“CAIDI”), System Average Interruption Duration Index (“SAIDI”), Average System Availability Index (“ASAI”) and Momentary Average Interruption Duration Index (“MAIFI”). Each index provides a measure, in terms of ratios or percentages, of interruptions in delivery of electrical power, wherein an interruption may be classified as a complete loss of electrical power or where the quality of the delivered electrical power falls below or exceeds a pre-determined threshold. SAIFI measures the ratio of the total number of customer interruptions to the total number of customers served, hence the average. Lower averages signify better reliability. CAIDI measures the total customer hours interrupted to the total customer interruptions, in minutes. The lower the measure, the better the reliability. SAIDI measures the ratio of customer hours interrupted to total customers served, in minutes. Again, the smaller the number the better the system. ASAI is a ratio of total number of customer hours the electric service has been turned on to the number of customer hours the service has been demanded. It is measured as a percentage and the higher the percentage, the better the reliability. The MAIFI measurement considers interruptions that last less than 5 minutes. System Average RMS Variation Frequency Index (“SARFI”) is another power quality index that provides counts or rates of voltage sags, swells and/or interruptions for the system. There are two types of SARFI indices—SARFI_x and SARFI_curve. SARFI_x corresponds to a count or rate of voltage sags, swells and/or interruptions below a threshold where SARFI_curve corresponds to a rate of voltage sags below an equipment compatibility curve, such as a CBEMA or SEMI curve. Mean Time Between Failure (“MTBF”) is another measurement to indicate reliability. MTBF is usually expressed in hours and is calculated by dividing the total number of failures into the total number of operating hours observed. For example a device may specify MTBF as 300,000 hours. If this device operates 24 hours a day, 365 days a year it would take an average of 34 years before the device will fail.
The present invention provides methods for tracking individual utility outages at the customer level. The difficulties inherit in the art are therefore overcome in a way that is simple and efficient, while providing better and more advantageous results.